Spiking neural networks (SNNs) can exchange information between neurons via electric pulses, referred to as spikes or spiking signals. Due to this spiking, the behavior of SNNs is more similar to the behavior of neuro-biological systems than the behavior of non-spiking artificial neural networks. Although SNNs exhibit powerful theoretical capabilities, their poor performance on traditional computer hardware has made them unsuitable for many practical applications. Recently, neuromorphic hardware devices have been developed that can improve the power consumption, intrinsic parallelism, and processing speed for many types of neural networks. However, adaptation of many classical neural network methodologies to neuromorphic hardware has proved to be difficult, if not impossible.
Therefore, there is room for improvement in technologies related to spiking neural networks in neuromorphic hardware systems.